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Generating structured music for bagana using quality metrics based on Markov models

机译:使用基于Markov模型的质量指标为Bagana生成结构化音乐

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In this research, a system is built that generates bagana music, a traditional lyre from Ethiopia, based on a first order Markov model. Due to the size of many datasets it is often only possible to get rich and reliable statistics for low order models, yet these do not handle structure very well and their output is often very repetitive. A first contribution of this paper is to propose a method that allows the enforcement of structure and repetition within music, thus handling long term coherence with a first order model. The second goal of this research is to explain and propose different ways in which low order Markov models can be used to build quality assessment metrics for an optimization algorithm. These are then implemented in a variable neighborhood search algorithm that generates bagana music. The results are examined and thoroughly evaluated. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在这项研究中,基于一阶马尔可夫模型,构建了一个系统,该系统可生成巴加纳音乐(一种来自埃塞俄比亚的传统里拉琴)。由于许多数据集的大小,通常仅可能获得低阶模型的丰富而可靠的统计信息,但是这些模型不能很好地处理结构,因此它们的输出通常非常重复。本文的第一个贡献是提出一种方法,该方法允许在音乐中强制执行结构和重复,从而利用一阶模型处理长期连贯性。这项研究的第二个目标是解释和提出不同的方法,在这些方法中低阶马尔可夫模型可用于建立优化算法的质量评估指标。然后,这些变量会在生成巴加那音乐的可变邻域搜索算法中实现。检查结果并进行彻底评估。 (C)2015 Elsevier Ltd.保留所有权利。

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